Accurate and reliable defect inspection is vital to the successful fabrication processes. Fabrication processes strive to faithfully reproduce an intended design, or standard, that is typically represented by the computer aided design (CAD) pattern created by a designer. It is generally known that deformation of the standard occurs in fabricating various works (i.e., making copies of the standard), such as artworks, reticles, photomasks and production parts of electronic, semiconductor, display, micro-electromechanical system, hard disk and nanotechnology workpieces, devices, and continuous web processes, among others. Fabrication processes are often highly sensitive to: 1) material defects; 2) contamination by particles introduced by various processing tools; 3) various deleterious effects associated with unwanted environmental contaminants; 4) residual films; 5) extra or missing materials; and 6) other defects resulting from fabrication process variations.
Typically, each layer (i.e., copy) in a production part has been formed to closely represent the specific structures intended according to that part's particular design, as represented by the standard. Often, a production part is produced through its own corresponding mask, reticle, photomask or artwork to properly form regions of light that are subsequently directed onto a substrate. Depending on the type of resist (e.g., positive or negative) that is coated upon the substrate, the regions of light formed by the mask correspond to either the specific structures formed on the substrate or the spaces between these structures. The corresponding mask, reticle, photomask or artwork is patterned in either a 1:1 representation of the standard, or a derivative of the standard that corresponds to the structures formed on the substrate. Other times, the production part is produced through the direct ablation, machining, milling, etching or deposition of material so that the result is representative of the designed data structure.
Conventional inspection systems are designed to identify defects by comparing an image of the artwork, reticle, photomask or production part, i.e., a copy, with a standard that is either the designed data structure (the designer's original intention), or a “golden” image of a part being inspected. Any dissimilarity is reported as a defect.
It has been known that spurious “false” defects are reported by all automatic optical inspection systems. The sources of the false defects are many, including those related to the production process of the piece to be inspected, the defect observation system, the defect image analysis methodology, the defect classification methodology and others. One approach to this problem, disclosed in U.S. Pat. No. 4,805,123 to Specht, et al., is to resample and detect the differences between the samples to reduce the effect of false reports. A different approach to this problem, disclosed in the U.S. Pat. No. 4,532,650 to Wihl, et al., uses an algorithm specific to defect types (defects at pattern corners). Yet another approach to this problem disclosed in U.S. Pat. No. 6,480,627 to Mathias et al., is to use an evolutionary (learning) algorithm implemented in a neural net that effects image characterization and classification.
Typical conventional inspection systems employ simple image, single-level analysis. The quality of the inspection can be considered to be limited to the signal-to-noise ratio achievable by the system. Inspection of artworks, reticles, photomasks and parts production of electronic, semiconductor, display, micro-electromechanical system, hard disk, and nanotechnology processes using the above described single stage process has a limited capability to reliably identify small sized defects of interest. While any single inspection technique may be very capable of detecting a certain type defect, it is less likely to be capable of detecting other types of defects. For any single inspection technique, a portion of the defects may be readily detectable, while another portion of the defects is either poorly detected or undetectable by the inspection system. A differing inspection technique may have different sensitivity to the same group of defects. Furthermore, any single inspection technique may accurately identify the presence of differing types of defects but be incapable of differentiating between the differing types of defects. Thus, what is needed is an improved method of defect detection and reporting technique for detecting and differentiating defects of interest that are difficult to accurately detect using current inspection techniques while minimizing the reporting of non-defective areas being reported as defective. The present invention satisfies that need, as well as others, and overcomes deficiencies in current inspection techniques.